No paywalls. No publication charges. Our AI-enhanced preprint network makes papers, data, and code permanently accessible for everyone. And store 100 GB of data for FREE!
Try Our Preprint Network
If so, upload your manuscript and sign up for DeSci Publish today!
It’s free to store up to 100 GBs of papers, data and code and takes less than two minutes to do.
DeSci Publish is an open-source preprint network for all types of research outputs. Making open science more convenient and rewarding than ever. No paywalls. No publication charges.
It's the first integrated preprint solution that supports any file type, as well as versionability. And the first publishing solution for science that leverages web3 technologies.
Simply upload your research and test DeSci Publish in less than two minutes. It’s free to store up to 100 GBs!
As a researcher in industry, DeSci Labs offers me the tools I need to publish my work in a format that other researchers can consume. By tying my identity to my ORCID, my academic credentials are legible, but I am not limited to submitting PDFs into archaic review processes. Instead, I can bundle my work up into a package of related materials including code, data, written analysis, and more. When I share bundles as nodes on Publish, I can claim attributes and have them evaluated by ARCs who specialize in relevant domains. Thanks to DeSci Labs, I am excited about publishing again.
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“Tailored to scientific publishing, Publish provides the unique opportunity to upload entire research objects, including code, data, manuscripts, and further material. This gives us junior researchers the convenience to receive early feedback on our work and reproduce each other’s studies without much effort. Taking advantage of the version tracking, I can then transparently implement feedback, which increases the trust in my study and reliability during later stages of publishing. Additionally, the visually appealing interface is fun to work with and I recommend it to any researcher who is committed to excellent and open scientific practice.”
Our submission package builder highlights the added value of your data and code, ensuring editors and readers recognize the full depth of your work—not just the manuscript.
Get publication credit on CrossRef & ORCID for sharing preprints, data, or code: Simply claim the Open Data or Open Code badge and generate a submission package. Once verified, a digital object identifier (DOI) will be minted, and the authors’ ORCID records updated: both entirely automatically.
Post your analysis plan, add data and code, and refine your manuscript over time—all while showcasing transparency your peers will value. Share early, share often, and secure credit for your ideas long before formal publication.
Our novelty scores allow you to explore the scientific literature from a new perspective. What papers are most innovative and likely to drive the future of research? You can filter your search results by topics, authors, journals, institutions, and dates - creating ad-hoc innovation rankings.
Soon, we will show you novelty scores for every version of your papers.
DeSci Publish is entirely based on open-source software that can be used or forked by everyone. All data is stored on an open, participatory network that enables data souvereignity and protects against link rot, content drift, loss of data, and data silos.
Megan turned her PhD thesis into a Research object, primarily to share her code with the astrophysics community. During her thesis, she took a lot of care to make her work reproducible. With Desci Publish she finally has a medium to showcase this effort and share the outcome with everyone. Desci Publish allows her to create interactive links between her paper, data, and code.
Atharva and Marco created a fully reproducible, data-heavy publication containing over 18 terabytes of open data. Without Desci Publish, this breakthrough dataset in fluid dynamics would have remained in his lab's internal servers. Desci Publish made it possible to easily import parts of this dataset with a single line of python, or send a containerised compute job directly to where the data lives.
